Otsu's method (1979) is a technique for optimum global thresholding that maximizes between-class variance by analyzing the histogram of an image. The basic global thresholding algorithm involves selecting an initial threshold, segmenting the image, and iteratively refining the threshold until convergence. The effectiveness of this method is highly dependent on the partitioning quality of the histogram.

3. Optimum Global Thresholding Using Otsu’s
Method
• Otsu’s method (1979) maximizes between-class variance
• Based entirely on computations performed on histogram (1-D) of
image
• Normalized histogram: pi = ni MN, i = 0, . . . , L − 1, with L − 1 i=0 pi =
1, pi > 0
• Select threshold T ( k ) to segment image −→ Class C 1 (values [0, k])
Class C2 (values [k + 1, L − 1] )
⇒ Prob of pixel assigned to C 1 (ie of C 1 occuring):

4. Optimum Global Thresholding Using Otsu’s
Method

6. Basic Global Thresholding
Based on the histogram of an image
Partition the image histogram using a single global threshold
The success of this technique very strongly depends on how well the
histogram can be partitioned

7. Basic Global Thresholding Algorithm
The basic global threshold, T, is calculated
as follows:
1. Select an initial estimate for T (typically the average grey level in the
image)
2. Segment the image using T to produce two groups of pixels: G1 consisting
of pixels with grey levels >T and G2 consisting pixels with grey levels ≤ T
3. Compute the average grey levels of pixels in G1 to give μ1 and G2 to give
μ2

8. Basic Global Thresholding Algorithm
4. Compute a new threshold value:
5. Repeat steps 2 – 4 until the difference in T in successive iterations is less
than a predefined limit T∞
This algorithm works very well for finding thresholds when the
histogram is suitable
2
2
1 
 

T